code: def preprocess_example(example, dtype=tf.float32):
      """Preprocesses the given example.
  
      Args:
        example: dict; Example that has an 'image' and a 'label'.
        dtype: Tensorflow data type; Data type of the image.
  
      Returns:
        A preprocessed example.
      """
      image = tf.cast(example["image"], dtype=dtype)
      if dtype not in [tf.int32, tf.int64, tf.uint32, tf.uint64]:
          mean_rgb = tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=dtype)
          std_rgb = tf.constant(
              STDDEV_RGB, shape=[1, 1, 3], dtype=dtype
          )
          image = (image - mean_rgb) / std_rgb
      return {"inputs": image, "label": example["label"]}
  
file_path: scenic/scenic/dataset_lib/cifar10_dataset.py
imports: import functools
  from typing import Optional
  from absl import logging
  import jax.numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess_example(example, dtype) and returns: dict